A reference implementation of a well-structured Machine Learning project.

Most ML practitioners start with notebooks and a couple of folders. It works — until the project grows. Then reproducibility breaks, pipelines become messy, and deploying models turns into chaos.

This repository shows how to organize ML projects like a professional software system: modular, reproducible, and production-ready. The structure is based on lessons learned from 10+ industrial-level ML projects.

Machine learning projects aren’t just about training models. You deal with:

• Multiple versions of datasets
• Experimental code evolving into production workflows
• Model artifacts that must be versioned and tracked
• Complex pipelines from raw data to predictions

Without structure, these complexities quickly spiral out of control.

This repo demonstrates how to design an ML project that is maintainable, reproducible, and ready for production.